what matters isn’t necessarily the exact dictionary definition of data as a product, but rather that data teams work diligently to find processes and systems that help them advocate for the importance of data on a wider, organizational level. (View Highlight)
• Data as a product is about … key product development principles.
• Data as a product is about … providing data to stakeholders automatically.
• Data as a product is about … applying the principles of product thinking.
• Data as a product is about … all the tools, processes, and people that go into it. (View Highlight)
data as a product is explained as the concept of applying key product development principles (such as identifying and addressing unmet needs, agility, iterability, and reusability) to data projects (View Highlight)
• The data team can understand and apply the product development principles above.
• The data team has developed point solutions that solve the specific needs of their stakeholders. (View Highlight)
Once a data team has abstracted the underlying principles of successful point solutions and reused them to solve an array of business problems, they have achieved data as a product. (View Highlight)
data as a product is about the concept of providing data to stakeholders in an automated fashion to facilitate good decision-making (View Highlight)
• DaaS professionals have specific domain expertise (marketing, finance, product, etc.) and are focused on providing insights.
• DaaP professionals are engineers. They are focused on building processes and data pipelines and delivering rows/columns of data to facilitate good decision-making. (View Highlight)
Product thinking: A mindset that’s outcome-oriented, business-capability aligned, long-lived, and cross-functional with the intention to solve problems and improve business outcomes. Additionally, there should be a focus on discoverability, security, explorability, understandability, trustworthiness, etc (View Highlight)
Data products: Using “raw data, derived data, [and] algorithms” to automate and/or guide decisions to improve business outcomes (View Highlight)
Under this view, every piece of data, the tools used to generate, access, and analyze, are integrated together as one big data product. Any internal tool used to make a decision is a feature of the data product. (View Highlight)
they simply call all company data data as a product, with the individual tools being features of data as a product while other articles are calling the individual featuresdata as a product. (View Highlight)
Data as a product is the concept of applying key product development principles (Identifying and addressing unmet needs, agility, iterability, and reusability) to data projects. (View Highlight)
Fundamentally, data as a product is a concept, or methodology, about how data teams can create value in their organizations. The general belief is that applying product management principles to data teams will make data work more valuable and scalable, qualities that have been lacking in the data community for years. (View Highlight)
